Privacy-Preserving Distributed Energy Resource Control with Decentralized Cloud Computing

TL;DR

This paper introduces a decentralized, privacy-preserving control framework for distributed energy resources that scales efficiently, secures user privacy, and outperforms existing methods in accuracy and computation.

Contribution

It proposes a novel decentralized control method integrating cloud computing and secret sharing to enhance scalability, privacy, and efficiency in DER management.

Findings

Achieves control scalability over large DER populations.

Secures privacy against various adversaries without peer-to-peer communication.

Improves computation efficiency and accuracy over existing methods.

Abstract

The rapidly growing penetration of renewable energy resources brings unprecedented challenges to power distribution networks - management of a large population of grid-tied controllable devices encounters control scalability crises and potential end-user privacy breaches. Despite the importance, research on privacy preservation of distributed energy resource (DER) control in a fully scalable manner is lacked. To fill the gap, this paper designs a novel decentralized privacy-preserving DER control framework that 1) achieves control scalability over DER population and heterogeneity; 2) eliminates peer-to-peer communications and secures the privacy of all participating DERs against various types of adversaries; and 3) enjoys higher computation efficiency and accuracy compared to state-of-the-art privacy-preserving methods. A strongly coupled optimization problem is formulated to control the power consumption and output of DERs, including solar photovoltaics and energy storage systems, then solved using the projected gradient method. Cloud computing and secret sharing are seamlessly integrated into the proposed decentralized computing to achieve privacy preservation. Simulation results prove the capabilities of the proposed approach in DER control applications.